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Computer-aided design and diagnosis methods for biomedical applications / edited by Varun Bajaj and G. R. Sinha.
Material type:
TextLanguage: English Publication details: Boca Raton : CRC Press, 2021.Edition: 1st edDescription: xxiii, 367 p. : ill. ; 26 cmISBN: - 9780367638832
- 610.28 VAR
Contents:
Summary: Present book discusses uses of CAD to solve real world problems and challenges in Biomedical systems with the help of appropriate case studies and research simulation results. It explains behaviours, concepts, fundamentals, principles, case studies and future research directions including automatic identification of related disorders using CAD.
Chapter 1 Electroencephalogram Signals Based Emotion Classification in Parkinson’s Disease Using Recurrence Quantification Analysis and Non-Linear ClassifiersChapter 2 Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG SignalsChapter 3 Detection of Epileptic Electroencephalogram Signals Employing Visibility Graph MotifsChapter 4 Effect of Various Standing Poses of Yoga on the Musculoskeletal System Using EMGChapter 5 Early Detection of Parkinson Disease and SWEDD Using SMOTE and Ensemble Chapter 6 Computer-Aided Design and Diagnosis Method for Cancer DetectionChapter 7 Automated COVID-19 Detection from CT Images Using Deep LearningChapter 8 Suspicious Region Diagnosis in the Brain: A Guide to Using Brain MRI SequencesChapter 9 Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural NetworkChapter 10 Positioning the Healthcare Client in Diagnostics and the Validation of Care IntensityChapter 11 Computer-Aided Diagnosis (CAD) System for Determining Histological Grading of Astrocytoma Based on Ki67 CountingChapter 12 Improved Classification Techniques for the Diagnosis and Prognosis of CancerChapter 13 Discovery of Thyroid Disease Using Different Ensemble Methods with Reduced Error Pruning TechniqueChapter 14 Reliable Diagnosis and Prognosis of COVID-19 Chapter 15 Computer-Aided Diagnosis Methods for Non-Invasive Imaging of Sub-Skin LesionsIndex
| Item type | Current library | Collection | Shelving location | Call number | Status | Barcode | |
|---|---|---|---|---|---|---|---|
Reference
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Kalaignar Centenary Library Madurai | ENGLISH-REFERENCE BOOKS | ஆறாம் தளம் / Sixth floor | 610.28 VAR (Browse shelf(Opens below)) | Not for loan | 223632 |
Includes index.
Includes bibliographical references.
Chapter 1 Electroencephalogram Signals Based Emotion Classification in Parkinson’s Disease Using Recurrence Quantification Analysis and Non-Linear ClassifiersChapter 2 Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG SignalsChapter 3 Detection of Epileptic Electroencephalogram Signals Employing Visibility Graph MotifsChapter 4 Effect of Various Standing Poses of Yoga on the Musculoskeletal System Using EMGChapter 5 Early Detection of Parkinson Disease and SWEDD Using SMOTE and Ensemble Chapter 6 Computer-Aided Design and Diagnosis Method for Cancer DetectionChapter 7 Automated COVID-19 Detection from CT Images Using Deep LearningChapter 8 Suspicious Region Diagnosis in the Brain: A Guide to Using Brain MRI SequencesChapter 9 Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural NetworkChapter 10 Positioning the Healthcare Client in Diagnostics and the Validation of Care IntensityChapter 11 Computer-Aided Diagnosis (CAD) System for Determining Histological Grading of Astrocytoma Based on Ki67 CountingChapter 12 Improved Classification Techniques for the Diagnosis and Prognosis of CancerChapter 13 Discovery of Thyroid Disease Using Different Ensemble Methods with Reduced Error Pruning TechniqueChapter 14 Reliable Diagnosis and Prognosis of COVID-19 Chapter 15 Computer-Aided Diagnosis Methods for Non-Invasive Imaging of Sub-Skin LesionsIndex
Present book discusses uses of CAD to solve real world problems and challenges in Biomedical systems with the help of appropriate case studies and research simulation results. It explains behaviours, concepts, fundamentals, principles, case studies and future research directions including automatic identification of related disorders using CAD.